Large-scale network-based services often require large-scale data storage. For example, Internet email services store large quantities of user inboxes, each user inbox itself including a sizable quantity of data. This large-scale data storage is often implemented in datacenters comprised of storage and computation devices. The storage devices are typically arranged in a cluster and include redundant copies. This redundancy is often achieved through use of a redundant array of inexpensive disks (RAID) configuration and helps minimize the risk of data loss. The computation devices are likewise typically arranged in a cluster.
In operation, clients accessing these large-scale services often need to interact with objects distributed across the data storage. For example, the clients may seek to create, extend, delete, read from, or write to byte sequences, such as binary large objects. Some of these operations, such as create, extend, and delete requests related to a same byte sequence, require coordination among multiple clients. For example, because a byte sequence can only be created once, only a single one of multiple create requests issued by clients for that byte sequence should succeed.
In order to coordinate these requests between the multiple clients, large-scale services typically utilize a central server that receives all requests requiring coordination and processes those requests serially. The central server also stores metadata for all of the byte sequences to enable the central server to, for example, determine if a byte sequence has already been created and determine a current size of the byte sequence. While utilization of the central server achieves coordination of the client requests and avoids the inconsistencies that would arise without coordination, it also introduces performance bottlenecks, as all of the clients must interact with the same central server and wait for it to serially respond to their requests.